class_docstr: Perceptron function
  
  Parameters
  ----------
  x: np.ndarray
      the input data
  y: np.ndarray
      the data to predict from the input data
docstr: Perceptron problem defined in
  Machine Learning and Data Analysis (MLDA) Problem Set v1.0, Gallagher et al., 2018, PPSN
  https://drive.google.com/file/d/1fc1sVwoLJ0LsQ5fzi4jo3rDJHQ6VGQ1h/view
  
  Parameters
  ----------
  name: str
      name of the dataset (among "quadratic", "sine", "abs" and "heaviside")
  
  Note
  ----
  quadratic is coined P2.a, sine P2.b, abs P2.c and heaviside P2.d
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code: def from_mlda(cls, name: str) -> "Perceptron":
      """Perceptron problem defined in
      Machine Learning and Data Analysis (MLDA) Problem Set v1.0, Gallagher et al., 2018, PPSN
      https://drive.google.com/file/d/1fc1sVwoLJ0LsQ5fzi4jo3rDJHQ6VGQ1h/view
  
      Parameters
      ----------
      name: str
          name of the dataset (among "quadratic", "sine", "abs" and "heaviside")
  
      Note
      ----
      quadratic is coined P2.a, sine P2.b, abs P2.c and heaviside P2.d
      """
      data = datasets.make_perceptron_data(name)
      pb = cls(data[:, 0], data[:, 1])
      pb.add_descriptors(name=name)
      return pb
  
imports: import numpy
  import pandas
  import scipy.spatial
  from nevergrad.parametrization import parameter
  import nevergrad.common.typing
  from ..base import ExperimentFunction
  from .None import datasets
label_desc: Use image node_id 2 for calling the Perceptron obj's underlying member method code with example usage: obj.from_mlda(cls, name) and returns: pb